More than 10,000 sensors and detectors are usually adopted in a typical nuclear power plant (NPP) unit for measurements and controls of nuclear and non-nuclear processes, radiation monitoring, and other special applications including vibration measurements, hydrogen concentration monitoring, water conductivity and boric acid concentration measurement, or failed fuel detection, etc. (IAEA, 1999). Among a wide range of different sensing technologies, piezoelectric sensing has been increasingly used in temperature measurement, vibration monitoring, pressure and flow rate measurement, hydrogen concentration measurement, detection of water level and structural defects (Korsah, et al., 2009), largely due to the nature of compact assembly of piezoelectric devices and free from electromagnetic interference. However, harsh environments (high temperatures, high radiations, etc.) in nuclear reactors and fuel cycle systems greatly challenge the existing NPP sensing technology. Specifically, existing piezoelectric (including acoustic sensing) sensing techniques are mostly limited to applications at temperatures up to <250°C, by their lack of radiation resistance, their lack of embed-ability because of the wired electric power supply and communication, and their unknown long-term performance behavior under harsh environments.
In this project, we develop 1) high temperature (> 600°C) embedded/integrated sensors (HiTEIS) for wireless monitoring of temperature, vibration, water level, pressure and structural integrity; 2) investigate remote acoustic charging of HiTEISs using laser ultrasound; 3) implement nuclear environment compatible secured remote communication for HiTEISs; 4) verify the HiTEIS technology in reactor and fuel cycle environments.
The remaining driving range of an electric vehicle (EV) is a complex function of various types of data with different formats that need to be extracted and collected from different sources using big data analytics. In our previous project, we had designed a big data framework to extract, classify, and pre-analyze the data regarding the route, terrain, driving behavior, weather, etc. from different resources, and find the correlations between the data and range estimation.
In this project, we Incorporate and analyze the ancillary power consumed mainly by the heating, ventilating, and air conditioner (HVAC) system in calculating the driving range of the vehicle using big data analysis techniques. Moreover, the current version of the algorithm estimates the range based on certain assumptions about the driving conditions of each trip such as weather, driving behavior, road condition, etc. However, the driving conditions are subject to change due to unpredicted events along the trip (such as accidents, sudden climate changes, etc.) which can make the predicted range inaccurate. Therefore, we design an adaptive structure to update the predicted power consumption using weather forecast and traffic report information in real-time, to compensate for the changes in environmental and driving conditions.
This project focuses on translating a novel Smart Battery Gauge technology to fill the increasing need for accurate battery state of charge (SOC) and remaining useful life (RUL) estimations for stationary energy storage of renewable energy. As compared to the existing battery monitoring methods, the reliably accurate estimation date generated by this technology will provide systems management and operations with the advantages of improved energy storage system efficiency, reliability, cost-effectiveness, longer lifespan, and reduced capital and operation/maintenance costs. In order to determine the technical feasibility and functional requirements of applying this technology in the stationary energy storage market and provide a commercially valuable solution, this project will result in a software prototype of the Smart Battery Gauge technology to demonstrate its real-time adaptive battery SOC and RUL estimations with market-leading accuracy and reliability, and its flexible customization for multiple different battery chemistries.
The objectives of this project are to:
1) Extract the relevant data and models that are needed for RUL estimation,
2) Design the adaptive predictive battery RUL estimation algorithm that can adjust battery parameters with real-time measurement feedback, and
3) Implement and demonstrate the Smart Battery Gauge technology prototype in software and benchmark its performance with existing approaches.
The efficiency, reliability, economics and sustainability of the production and distribution of electricity are the major concerns of all the power utilities and customers. Without large investments in new transmission capacity and generation sources to keep up with the growth in demand and corresponding investment, the existing early-twentieth-century-designed power grids would face a lot of challenges. An innovative and economical way of solving these challenges is the introduction of “microgrid” concept. Microgrids are electricity distribution systems consisting of local loads and distributed energy resources such as distributed generators, storage devices, and responsive demands as cheap spinning reserves. This flexibility of generation and demand available in the microgrids along with the access to renewables, can dramatically improve the power quality and reliability of the power networks at the distribution level. However, economically and intelligently managing these energy resources to improve the power quality, maximize the benefit of the unit owners while ensuring their privacy, requires advanced optimization and control technologies.
The objective of ADAC Distributed Intelligent Energy Management (DIEM) project is to design a robust cooperative distributed energy management framework for the microgrids to coordinate and schedule energy generation, energy storage and energy demand at different levels to achieve global optimal in terms of economic operation while satisfying the quality constraints. The envisioned framework is scalable, robust to communication imperfections (such as time delay, packet losses), and robust to single point of failures.
Because of the distributed nature of the controllable devices (e.g., distributed generations and controllable loads), agent-based system modeling is used by utilizing the graph theory. Assuming a two-way communications among distributed controllable devices, the system can be modeled using adjacency matrix based on the availability of communication link between each pair of agents. Based on the communications network, consensus and gossip based algorithm are applied for the development of the DIEM algorithms.
N. Rahbari-asr, Y. Zhang, and M. Chow, “Stability Analysis for Cooperative Distributed Generation Dispatch in a Cyber-Physical Environment,” in IECON 2014 – 40th Annual Conference on IEEE Industrial Electronics Society, Dallas, TX, USA, 2014.
Y. Zhang and M.-Y. Chow, “Hybrid Incremental Cost Consensus Algorithm for Smart Grid Distributed Energy Management under Packet Loss Environment,” in IECON 2014 – 40th Annual Conference on IEEE Industrial Electronics Society, Dallas, TX, USA, 2014.
Intelligent Space (iSpace) is a relatively new concept to effectively use distributed sensors, actuators, robots, computing processors, and information technology over communication networks. iSpace is a large scale Mechatronics System by integrating sensors, actuators, and control algorithms in a communication system using knowledge from various engineering disciplines such as automation, control, hardware and software design, image processing, communication and networking.
Kelola interaksi pelanggan secara efisien lewat sistem CRM MTP di KAYARAYA, yang dirancang untuk menyatukan data dan strategi dalam satu dasbor cerdas.
